[Enhancement][CUDA] Support nvidia-cuda-nvcc as nvcc
#1528
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📝 WalkthroughWalkthroughAdds CUDA_HOME discovery from the Changes
Sequence Diagram(s)sequenceDiagram
participant Caller as Caller
participant Env as tilelang.env._find_cuda_home
participant PATH as System PATH (nvcc)
participant Package as importlib.metadata.files("nvidia-cuda-nvcc")
participant FS as Filesystem
Caller->>Env: request CUDA_HOME
Env->>Env: check CUDA_HOME env var & known locations (Guesses `#1`)
Env->>PATH: is nvcc on PATH? (Guess `#2`)
alt nvcc on PATH
PATH-->>Env: nvcc path
Env->>FS: resolve grandparent -> candidate cuda_home
else nvcc not on PATH
Env->>Package: inspect package files (Guess `#3`)
alt package provides nvcc
Package-->>Env: nvcc file path
Env->>FS: resolve grandparent -> candidate cuda_home
else
Env->>FS: fallback checks for standard CUDA installs (Guess `#4`)
end
end
Env->>FS: validate candidate cuda_home (unified step)
alt valid
Env-->>Caller: CUDA_HOME (path)
else invalid
Env-->>Caller: "" (empty)
end
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Possibly related PRs
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🧹 Nitpick comments (1)
tilelang/env.py (1)
91-93: Non-deterministic CUDA version selection when multiple versions are installed.
glob.glob()returns paths in arbitrary filesystem order. If multiple CUDA versions are installed (e.g.,v11.8,v12.0,v12.4), selectingcuda_homes[0]gives unpredictable results across runs or machines.🔎 Proposed fix to prefer the latest CUDA version
if sys.platform == "win32": cuda_homes = glob.glob("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*") - cuda_home = "" if len(cuda_homes) == 0 else cuda_homes[0] + if cuda_homes: + # Sort to prefer the latest version (e.g., v12.4 over v11.8) + cuda_homes.sort(reverse=True) + cuda_home = cuda_homes[0] + else: + cuda_home = ""
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tilelang/env.py
🧰 Additional context used
🧠 Learnings (2)
📓 Common learnings
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:27.444Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
📚 Learning: 2025-12-24T17:20:27.444Z
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:27.444Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
Applied to files:
tilelang/env.py
🔇 Additional comments (2)
tilelang/env.py (2)
69-87: LGTM - PyPI package detection correctly implements nvidia-cuda-nvcc paths.The candidate paths (
nvidia/cu13/bin/,nvidia/cu12/bin/,nvidia/cu11/bin/) correctly match the nvidia-cuda-nvcc PyPI package installation structure. The priority order prefers newer CUDA versions, and the fallback tonvidia/cuda_nvcc/bin/is a reasonable defensive addition. Based on learnings from the previous PR.
98-99: Verify HPC SDK fallback path is intentional.The path
/opt/nvidia/hpc_sdk/Linux_x86_64is the HPC SDK root, not a valid CUDA_HOME. The actual CUDA installation within HPC SDK is typically nested at/opt/nvidia/hpc_sdk/Linux_x86_64/<version>/cuda/<cuda_version>.This differs from Guess #2's handling (lines 62-64), which navigates from the nvcc binary to find the correct SDK path. If this fallback is reached, it's unlikely to point to a usable CUDA installation—though the validation at lines 101-103 will safely reject it.
Is this a best-effort guess that relies on the subsequent validation, or should this path be removed/adjusted for consistency?
oraluben
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This idea LGTM, with this PR, tilelang can work with a plain torch installation (with nvidia-cuda-nvcc installed), without requiring cuda toolchain.
I think we can also put nvidia-cuda-nvcc in optional dependency.
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🧹 Nitpick comments (2)
tilelang/env.py (2)
69-78: Move import to module level for consistency.The
importlib.utilimport is placed inside the function. Consider moving it to the top of the file alongside other imports for better consistency and readability.🔎 Proposed refactor
At the top of the file, add the import:
from __future__ import annotations import sys import os import pathlib import logging import shutil import glob +import importlib.util from dataclasses import dataclassThen remove it from inside the function:
if cuda_home is None: # Guess #3 # from pypi package nvidia-cuda-nvcc, nvidia-cuda-nvcc-cu12, etc. - import importlib.util - for submodule in ["cu13", "cu12", "cu11", "cuda_nvcc"]:
82-84: Consider sorting CUDA versions when multiple installations exist.The Windows fallback uses
glob.globwhich returns matches in arbitrary filesystem order. If multiple CUDA versions are installed, the selected version may be unpredictable. Consider sorting the results to select the latest version consistently.🔎 Proposed fix to select the latest CUDA version
if sys.platform == "win32": cuda_homes = glob.glob("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*") - cuda_home = "" if len(cuda_homes) == 0 else cuda_homes[0] + if len(cuda_homes) == 0: + cuda_home = "" + else: + # Sort to get the latest version (e.g., v12.8 comes after v11.8) + cuda_homes.sort(reverse=True) + cuda_home = cuda_homes[0]
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pyproject.tomltilelang/env.py
🧰 Additional context used
🧠 Learnings (2)
📓 Common learnings
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:27.444Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
📚 Learning: 2025-12-24T17:20:27.444Z
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:27.444Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
Applied to files:
pyproject.tomltilelang/env.py
🔇 Additional comments (4)
tilelang/env.py (3)
86-90: LGTM!The Linux/macOS fallback logic correctly checks standard CUDA installation paths in a reasonable priority order.
92-96: LGTM!The path validation logic correctly ensures that only existing CUDA installations are returned, with proper fallback to an empty string for backward compatibility.
74-78: The CUDA_HOME detection from PyPI packages at lines 74-78 is correct. Thespec.submodule_search_locations[0]returns thenvidia/cu13(or cu12/cu11) directory, which is the proper CUDA_HOME location containing thebin/subdirectory with nvcc. No changes needed.pyproject.toml (1)
55-59: The specified versions for both optional dependencies are valid and available on PyPI:
- nvidia-cuda-nvcc 13.0.48 ✓ exists (released Aug 4, 2025)
- nvidia-cuda-cccl 13.0.50 ✓ exists (released Aug 4, 2025)
No known security vulnerabilities were found for these versions.
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Actionable comments posted: 2
🧹 Nitpick comments (2)
tilelang/env.py (2)
93-95: Consider simplifying the validation logic.The validation can be streamlined since checking
cuda_home is Noneand then setting it toNoneis redundant.🔎 Proposed simplification
- # Validate found path - if cuda_home is None or not os.path.exists(cuda_home): - cuda_home = None + # Validate found path exists + if cuda_home and not os.path.exists(cuda_home): + cuda_home = None
72-72: Consider moving the import to the module level for better clarity.While importing
importlib.utilinside the function works, placing it at the module level (lines 1-8) would improve code organization and make dependencies more visible.🔎 Proposed change
At the top of the file (after line 7):
import shutil import glob +import importlib.util from dataclasses import dataclassThen remove the import from line 72:
if cuda_home is None: # Guess #3 # from pypi package nvidia-cuda-nvcc, nvidia-cuda-nvcc-cu12, etc. - import importlib.util if importlib.util.find_spec("nvidia") is not None:
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tilelang/env.py
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📓 Common learnings
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:27.444Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
📚 Learning: 2025-12-24T17:20:27.444Z
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:27.444Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
Applied to files:
tilelang/env.py
🔇 Additional comments (1)
tilelang/env.py (1)
69-79: The fix correctly prevents exceptions when nvidia packages are not installed.The guard clause at line 74 (
if importlib.util.find_spec("nvidia") is not None:) successfully prevents accessing nvidia submodules when the parent package is missing. Testing confirms thatimportlib.util.find_spec("nvidia")returnsNonewithout raising an exception when the package is not installed, and the conditional structure ensures submodule searches (lines 75-79) never execute in this scenario.
Would you mind to make this work (e.g. Here's my workaround for autodetection failure: |
|
@oraluben which Dockerfile are you using? |
I ran into the error with |
|
Sorry I have trouble in setting up a docker with libcuda.so.1 to reproduce (either could not run docker, or doesn't have GPU), could you help run this in your docker python -c "import nvidia.cu13; print('1: done')"
python -c "import tilelang; print('2:', repr(tilelang.env.CUDA_HOME))"
python -c "import os; print('3:', os.environ.get('CUDA_HOME', '<not present>'))"
python -c "import os; print('4:', os.environ.get('CUDA_PATH', '<not present>'))"An idea might you have your CUDA_HOME accidentally set to empty string so it wouldn't pass |
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I've validated that this PR works as expected, i.e., tilelang works from a raw ubuntu container with docker run --gpus and pip install 'tilelang[nvcc]' (it's the wheel name, not tilelang to be precise). cc @LeiWang1999
With some comments.
oraluben
changed the title
nvidia-cuda-nvcc as nvcc
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Actionable comments posted: 1
♻️ Duplicate comments (1)
tilelang/env.py (1)
81-92: Windows CUDA detection remains non-deterministic when multiple versions are installed.As flagged in the previous review,
glob.glob()does not guarantee a deterministic ordering of results. When multiple CUDA installations exist, the code picks an arbitrary match, which could lead to inconsistent behavior across runs or environments.🔎 Proposed fix to select the latest CUDA version
if cuda_home is None: # Guess #4 if sys.platform == "win32": cuda_homes = glob.glob("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*") - cuda_home = "" if len(cuda_homes) == 0 else cuda_homes[0] + if cuda_homes: + # Sort to get the latest version (e.g., v12.3 over v11.8) + cuda_home = sorted(cuda_homes)[-1] + else: + cuda_home = None else:
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tilelang/env.py
🧰 Additional context used
🧠 Learnings (2)
📓 Common learnings
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:32.819Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
📚 Learning: 2025-12-24T17:20:32.819Z
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:32.819Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
Applied to files:
tilelang/env.py
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tilelang/env.py
Outdated
| else: | ||
| cuda_home = os.path.dirname(os.path.dirname(nvcc_path)) | ||
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| if cuda_home is None: |
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This if cuda_home is None: is duplicated with previous if, please merge this (and Guess 4 below) with the previous one. This can also remove unnecessary indent changes in Guess 4 to keep the diff minimal.
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NO. Please check twice before you comment.
Not every single line is unnecessary. It's easy to figure out.
If you consist it is unnecessary, please use "Add a suggestion" to show correct code your thought.
Co-authored-by: Yichen Yan <oraluben@outlook.com>
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Actionable comments posted: 1
Fix all issues with AI Agents 🤖
In @tilelang/env.py:
- Around line 77-86: The code currently uses `assert False` in the branch where
`_get_package_version("nvidia-cuda-nvcc")` is present but no `nvcc` file is
found; replace that `assert False, "`nvidia-cuda-nvcc` installed but no `nvcc`
found"` with an explicit `raise AssertionError("nvidia-cuda-nvcc installed but
no nvcc found")` so the failure cannot be skipped under Python -O optimizations;
update the branch in the loop that inspects
`importlib.metadata.files("nvidia-cuda-nvcc")` and ensure `cuda_home` remains
untouched when raising the AssertionError.
🧹 Nitpick comments (1)
tilelang/env.py (1)
90-92: Consider sorting CUDA versions on Windows for deterministic selection.When multiple CUDA versions are installed,
glob.glob()doesn't guarantee ordering, which could lead to inconsistent behavior. Sorting the results and selecting the latest version would make the detection more predictable.🔎 Proposed enhancement
if sys.platform == "win32": cuda_homes = glob.glob("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*") - cuda_home = "" if len(cuda_homes) == 0 else cuda_homes[0] + if cuda_homes: + # Sort to deterministically select the latest CUDA version + cuda_home = sorted(cuda_homes)[-1] + else: + cuda_home = None else:Note: This is a pre-existing issue and not introduced by this PR.
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tilelang/env.py
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🧠 Learnings (2)
📓 Common learnings
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:32.819Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
📚 Learning: 2025-12-24T17:20:32.819Z
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:32.819Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
Applied to files:
tilelang/env.py
🪛 Ruff (0.14.10)
tilelang/env.py
86-86: Do not assert False (python -O removes these calls), raise AssertionError()
Replace assert False
(B011)
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- GitHub Check: Build wheels for Python 3.9 on ubuntu-latest with CUDA-12.8
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🔇 Additional comments (4)
tilelang/env.py (4)
2-2: LGTM!The
importlib.metadataimport is necessary for the new package version detection and file enumeration functionality.
48-52: LGTM!The helper function cleanly encapsulates package installation checks and avoids repetitive exception handling at call sites.
88-98: LGTM!The refactoring correctly structures the fallback logic to only run when nvcc is neither in PATH nor available via the
nvidia-cuda-nvccpackage. The control flow is clear and maintains the original behavior.
100-102: LGTM!The consolidated validation logic is cleaner and ensures all detection paths are validated consistently before returning.
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♻️ Duplicate comments (1)
tilelang/env.py (1)
90-92: Fix Windows CUDA detection non-determinism.When multiple CUDA versions are installed,
glob.glob()returns results in an arbitrary order, causingcuda_homes[0]to select a non-deterministic version. This can lead to inconsistent behavior across runs or environments.This issue was flagged in previous reviews and remains unaddressed.
🔎 Proposed fix to deterministically select the latest CUDA version
if sys.platform == "win32": cuda_homes = glob.glob("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*") - cuda_home = "" if len(cuda_homes) == 0 else cuda_homes[0] + if cuda_homes: + # Sort to deterministically pick the latest version (e.g., v12.3 over v11.8) + cuda_home = sorted(cuda_homes)[-1] + else: + cuda_home = None else:
🧹 Nitpick comments (1)
tilelang/env.py (1)
77-86: LGTM! nvidia-cuda-nvcc detection is well-implemented.The logic correctly detects CUDA installations from the
nvidia-cuda-nvccPyPI package by inspecting package files and deriving CUDA_HOME from the nvcc binary location. Theor []fallback safely handles the case wherefiles()returnsNone.However, consider extracting the exception message to a module-level constant to address the Ruff TRY003 hint and improve maintainability:
🔎 Optional refactor to address TRY003
At the top of the file (after logger definition):
_NVCC_NOT_FOUND_ERROR = "`nvidia-cuda-nvcc` installed but no `nvcc` found"Then update line 86:
else: - raise AssertionError("`nvidia-cuda-nvcc` installed but no `nvcc` found") + raise AssertionError(_NVCC_NOT_FOUND_ERROR)
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tilelang/env.py
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🧠 Learnings (2)
📓 Common learnings
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:32.819Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
📚 Learning: 2025-12-24T17:20:32.819Z
Learnt from: clouds56
Repo: tile-ai/tilelang PR: 1527
File: tilelang/env.py:0-0
Timestamp: 2025-12-24T17:20:32.819Z
Learning: The nvidia-cuda-nvcc PyPI package installs to `nvidia/cu13/bin/` (for CUDA 13), `nvidia/cu12/bin/` (for CUDA 12), and `nvidia/cu11/bin/` (for CUDA 11) in the site-packages directory, not to `nvidia/cuda_nvcc/bin/`. These paths should be used when detecting CUDA installations from PyPI packages in tilelang/env.py.
Applied to files:
tilelang/env.py
🪛 Ruff (0.14.10)
tilelang/env.py
86-86: Avoid specifying long messages outside the exception class
(TRY003)
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🔇 Additional comments (2)
tilelang/env.py (2)
48-52: LGTM! Clean package version detection helper.The implementation correctly handles the
PackageNotFoundErrorexception and returnsNonewhen the package is not installed, making it a reliable check for package presence.
100-104: LGTM! Consolidated validation improves code structure.The refactored validation logic consolidates path checking in a single location after all detection attempts, reducing duplication and improving maintainability. The final return ensures a consistent string return type (empty string for failure cases).
3 participants
Sorry last PR #1527 was closed by mistake, and my branch is also lost, so I prepared a new PR.
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